Scoping Review GuideSAFE

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---
name: scoping-review-guide
description: "Scoping review methodology for broad evidence mapping"
metadata:
  openclaw:
    emoji: "🔭"
    category: "research"
    subcategory: "deep-research"
    keywords: ["scoping review", "scoping study", "rapid review", "umbrella review"]
    source: "wentor-research-plugins"
---

# Scoping Review Guide

Conduct scoping reviews to map the breadth and nature of research evidence on a topic, using the Arksey & O'Malley framework and JBI methodology with PRISMA-ScR reporting.

## Scoping Review vs. Systematic Review

| Feature | Scoping Review | Systematic Review |
|---------|---------------|-------------------|
| **Purpose** | Map the evidence landscape | Answer a specific clinical/research question |
| **Question** | Broad, exploratory | Focused, narrow |
| **Inclusion criteria** | Broadly defined, may evolve | Strictly predefined |
| **Quality assessment** | Optional (not always done) | Required (risk of bias) |
| **Synthesis** | Descriptive/thematic mapping | Quantitative (meta-analysis) or narrative |
| **Protocol registration** | Recommended (OSF) | Required (PROSPERO) |
| **Reporting guideline** | PRISMA-ScR | PRISMA 2020 |

### When to Choose a Scoping Review

- To examine the extent, range, and nature of research activity on a topic
- To determine whether a full systematic review is warranted
- To identify key concepts, evidence gaps, and types of available evidence
- To map the research landscape before designing a primary study
- When the topic is too broad or heterogeneous for a systematic review

## Arksey and O'Malley Framework (5 Stages)

### Stage 1: Identifying the Research Question

Scoping review questions are broad and use the PCC framework:

```
Population:  Who is being studied?
Concept:     What is the key concept or phenomenon?
Context:     In what setting or discipline?

Example question:
"What is known about the use of AI tools in undergraduate
STEM education, including types of tools, pedagogical
approaches, and reported outcomes?"
```

### Stage 2: Identifying Relevant Studies

Conduct a comprehensive search across multiple sources:

```
Search strategy development:
1. Identify key terms from the PCC framework
2. Develop synonyms and related terms for each concept
3. Combine using Boolean operators

Example search string (PubMed):
("artificial intelligence" OR "machine learning" OR "deep learning"
 OR "natural language processing" OR "chatbot" OR "intelligent tutoring")
AND
("undergraduate" OR "higher education" OR "university student"
 OR "college student")
AND
("STEM" OR "science education" OR "engineering education"
 OR "mathematics education" OR "computer science education")

Databases to search:
- Discipline-specific databases (ERIC, PubMed, IEEE Xplore, etc.)
- Multidisciplinary databases (Scopus, Web of Science)
- Grey literature sources (ProQuest Dissertations, conference proceedings)
- Reference lists of included studies
```

### Stage 3: Study Selection

Develop and apply inclusion/exclusion criteria iteratively:

```markdown
| Criterion | Inclusion | Exclusion |
|-----------|-----------|-----------|
| Population | Undergraduate STEM students | K-12, graduate, non-STEM |
| Concept | AI-based educational tools | Non-AI technology (e.g., basic LMS) |
| Context | Formal educational settings | Informal learning, self-study apps |
| Study type | Empirical research (any design) | Editorials, opinion pieces |
| Language | English, Chinese | Other languages |
| Date | 2015-2025 | Before 2015 |
```

Screening process:
1. Import all records into a reference manager or screening tool (Rayyan, Covidence)
2. Remove duplicates
3. Title/abstract screening by two reviewers (independently recommended but not always required)
4. Full-text screening with documented exclusion reasons
5. Pilot screening on 50-100 records to calibrate inclusion criteria

### Stage 4: Charting the Data

Create a data charting form to extract standardized information:

```python
# Example: Data charting template as a structured dictionary
charting_template = {
    "study_id": "",           # Author, year
    "country": "",            # Country where study was conducted
    "study_design": "",       # RCT, quasi-experimental, case study, survey, etc.
    "sample_size": 0,
    "population": "",         # Student demographics
    "ai_tool_type": "",       # Chatbot, ITS, NLP-based, etc.
    "ai_tool_name": "",       # Specific tool name (e.g., ChatGPT, ALEKS)
    "subject_area": "",       # Physics, CS, Math, Biology, etc.
    "pedagogical_approach": "",  # Flipped classroom, adaptive learning, etc.
    "outcome_measures": [],   # Learning gains, engagement, satisfaction, etc.
    "key_findings": "",       # Brief summary of main results
    "limitations": ""         # Reported limitations
}
```

### Stage 5: Collating, Summarizing, and Reporting Results

Present results using multiple formats:

**Descriptive numerical summary**:
- Number of studies by year of publication
- Geographic distribution
- Study designs used
- AI tool types
- Outcome categories

**Thematic analysis**:
- Group findings into themes
- Identify patterns, trends, and gaps
- Map the conceptual landscape

```python
import pandas as pd
import matplotlib.pyplot as plt

# Example: Visualize publication trends
df = pd.read_csv("charted_data.csv")

# Publications by year
year_counts = df["year"].value_counts().sort_index()
fig, ax = plt.subplots(figsize=(10, 5))
ax.bar(year_counts.index, year_counts.values, color="#0072B2")
ax.set_xlabel("Publication Year")
ax.set_ylabel("Number of Studies")
ax.set_title("Included Studies by Year")
plt.tight_layout()
plt.savefig("studies_by_year.pdf", dpi=300)

# Evidence map: cross-tabulation
evidence_map = pd.crosstab(df["ai_tool_type"], df["outcome_measures"])
print(evidence_map)
```

## Other Review Types

### Rapid Review

A streamlined systematic review with methodological shortcuts to produce evidence within a compressed timeline (typically 2-6 months

SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.

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Findings (0)

No findings outside the package's declared scope.

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